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Description
Transformers is a versatile library that includes pretrained models for natural language processing, computer vision, audio, and multimodal tasks, facilitating both inference and training. With the Transformers library, you can effectively train models tailored to your specific data, create inference applications, and utilize large language models for text generation. Visit the Hugging Face Hub now to discover a suitable model and leverage Transformers to kickstart your projects immediately. This library provides a streamlined and efficient inference class that caters to various machine learning tasks, including text generation, image segmentation, automatic speech recognition, and document question answering, among others. Additionally, it features a robust trainer that incorporates advanced capabilities like mixed precision, torch.compile, and FlashAttention, making it ideal for both training and distributed training of PyTorch models. The library ensures rapid text generation through large language models and vision-language models, and each model is constructed from three fundamental classes (configuration, model, and preprocessor), allowing for quick deployment in either inference or training scenarios. Overall, Transformers empowers users with the tools needed to create sophisticated machine learning solutions with ease and efficiency.
Description
RoBERTa enhances the language masking approach established by BERT, where the model is designed to predict segments of text that have been deliberately concealed within unannotated language samples. Developed using PyTorch, RoBERTa makes significant adjustments to BERT's key hyperparameters, such as eliminating the next-sentence prediction task and utilizing larger mini-batches along with elevated learning rates. These modifications enable RoBERTa to excel in the masked language modeling task more effectively than BERT, resulting in superior performance in various downstream applications. Furthermore, we examine the benefits of training RoBERTa on a substantially larger dataset over an extended duration compared to BERT, incorporating both existing unannotated NLP datasets and CC-News, a new collection sourced from publicly available news articles. This comprehensive approach allows for a more robust and nuanced understanding of language.
API Access
Has API
API Access
Has API
Pricing Details
$9 per month
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
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iPhone App
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Windows
Mac
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Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Hugging Face
Founded
2016
Country
United States
Website
huggingface.co/docs/transformers/en/index
Vendor Details
Company Name
Meta
Founded
2004
Country
United States
Website
ai.facebook.com/blog/roberta-an-optimized-method-for-pretraining-self-supervised-nlp-systems/